RNN-based methods focus on temporal coherence in a sequence. Deterministic RNN-based methods made simplifying assumption there is only one plausible future. However, this can lead to low-quality predictions in real-world settings with stochastic dynamics. Shi et al. xingjian2015convolutional introduced ConvLSTM network for precipitation nowcasting. They added convolution operations in recurring connections of LSTM [1]}. This model was used by many recent precipitation nowcasting methods [2]}, [3]}, [4]}, [5]}, [6]}. Yao et al. yao2020prediction added two discriminators to ConvLSTM model in order to ensure continuity of video and realistic of photo. They proposed also an adaptive mean squared error (MSE) loss to reduce the influence of zero value pixels in precipitation maps. Other recent methods such [7]}, [8]} used ConvGRU [9]} which is a variant of the GRU model [10]} that leverages the convolution operations to encode spatial information. Bonnet et al. bonnet2020precipitation used PredRNN++ model [11]} which is an improved version of PredRNN model [12]}. In fact, PredRNN proposed Spatiotemporal LSTM unit (ST-LSTM) that contains double memory mechanism combining time memory updated horizontally with the spatial memory transformed vertically. PredRNN++ makes PredRNN model deeper in time by leveraging a new recurrent structure named Causal LSTM with cascaded dual memories. Some methods [13]}, [14]} used TrajGRU [15]} model that can actively learn the location-variant structure for recurrent connections. Tra et al. tran2019computer included SSIM and multi-scale SSIM to train their models. The predicted radar maps generated by deterministic RNN-based methods are blurry. To handle this problem, stochastic RNN-based methods predict a different possible future for each sample of its latent variables. Bihlo et al. [16]} used SVG-LP [17]} model for precipitation nowcasting. They used the prior model to learn the basic physical rules that precipitation cells evolve and move in time and space.
